System for Controlling and Optimizing Information Distribution Between Users in an Information Exchange

ABSTRACT

An automatic control system for regulating the information exchange between information producer and information consumer. One control mechanism can dynamically refine the decision to include or exclude information items from the consumer information stream to improve success metrics like participation. One or more system interface request control mechanisms can dynamically provide incentive and limits for the input of audience targets, priorities, preferences, and other data. An administrator may set parameters and select success metrics to balance the goals of the information exchange participants and stakeholders. The system can also serve to resolve conflicts between the selection criteria of a consumer and the audience targets of a producer.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of provisional patent applicationarticle number EI726604039US Filed Mar. 15, 2013.

BACKGROUND

A major drawback in services where users or members exchangeinformation, such as social networks, user groups, list servers, forums,question and answer services, and the like, is the inability to moreprecisely and optimally regulate the flow of information betweenproducers and consumers. Some practices are in place to make theseexchanges manageable and relevant to participants, but these lack theautomated dynamic refinements needed to potentially optimize orgenerally improve the objectives of the stakeholders. Other serviceswhere one group is exchanging information with another group such asnews aggregation services, newspapers, magazines, media, ad networks,blogs, research services, and the like face a similar problem.

One of the partial solutions used by many information exchanges is toadd group, tags, or topics that information consuming users cansubscribe to or use to filter the set of information available to them.This is an improvement not a full solution as increasing the number oftopics to decrease the rate of information produced per topic but stillleaves an inefficiency as consuming users must choose between lower rateof information flow and potentially losing some valuable informationitems from peripheral topics. Once they subscribe to the peripheraltopics the information rate and value dilution increases. Even if ainformation consumer's interests are contained in a single topic therewill still be a degree of variability of interest that could lead to aninefficiency particularly if there are many information items in thegiven topic.

Another problem with relying only on the topic approach is gettinginformation consuming users to specify a selection of topics. This isparticularly problematic in light of changing and evolving ontologies oftopics. Techniques are often employed to gain preferences or intereststhat are reveled from prior actions of activities of the users. A widevariety of methodologies are available, both public and proprietary, toidentify items of interest based on past behaviors and interactions (forexample click and view histories), collaborative filteringrecommendations, machine learning, and others. These methods yield a setof preferences for the information consumer that may be in conflict orhave varying ranges of applicability and accuracy of results. Theuncertainty of the derived preference will vary as well. To accommodatethese types of scenarios preferences are often ranked and applied inorder of ranking. This approach has limits by not considering dynamicexternal factors, the state of the information exchange, the producer ofthe information item and their targeting preferences for the informationitem. These and other factors might have an influence on theapplicability of the preference of the information consumer particularlywhen there is prediction uncertainty of the derived preference.

Another contributing problem is the practice of many informationexchanges to reduce or eliminate any restrictions for informationproducers to enter information items. This approach encourages quantitybut also leads to variable quality of contributions that ultimatelylower the value to the potential consumers in the exchange andextenuates the problems stated heretofore. This situation may not bealtered significantly even when there is a monetary assessment on thecontribution. While payment is a restriction that may correlate withquality it does not assure that a more optimal quality level isachieved.

While a number of ways are available to control the information flow tousers and allow users to self throttle the information flow, they aremostly suboptimal, non-dynamic, and ineffective in many cases.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1. Describes an example of an information exchange

FIG. 2. Describes an example of the producer interactions

FIG. 3. Describes an example of the interactions of a general user

FIG. 4. Describes an example of the interactions of the consumer

FIG. 5. Describes an example of a basic decision matrix with not actionpriorities shows audience targets down the vertical for producer andalong the horizontal.

FIG. 6. An exemplary embodiment of decision matrix with actionpriorities

FIG. 7. An exemplary embodiment with continuous priorities.

FIG. 8. Describes include region and threshold line for a decision gridor decision matrix

FIG. 9. Describes the audience size limits

FIG. 10. Describes the system interface for inputting the audiencetargets

DETAILED DESCRIPTION

An example of an information exchange 29 is shown in FIG. 1. A user 20of the information exchange 29 may be either a information producer 22or an information consumer 28 or both. The information exchange 29delivers an information item 24 from the information producer 22 to theinformation consumer 28. In the most general definition a informationexchange consists of one or more producers, one or more consumers and adistributor 26. The distributor 26 specifies how the information itemsflow from producer to consumer.

The distributor 26 can take multiple forms, for example an informationswitch including simple pass through, publisher to consumer, sender toreceiver, publish-subscribe, or any other form where information istransferred from a producer to a consumer. The distributor 26 wouldinclude for example cases where the consumer friends or follows one ormore producers or joins a group or where a producer and consumer haveagreed to follow or friend or exchange information with each other andallow the other party to do the same. The distributor 26 may supportsubscriptions or not. If subscriptions are supported, the consumer 28may be subscribed to one, several or all producers. If the distributor26 does not support subscriptions the consumer 28 will be able toreceive from all producers. There may be one or multiple producer 22.There may be one or multiple consumer 28. The information exchange 29could be a social network, a group within a social network, a listserver, a news aggregation service, a news feed, a newsletter, a digest,offers, alerts, an ad exchange, an ad network, email client, newsreader, web browser, portal or any service that facilitates a flow ofinformation items from producers to consumers.

The producer 22 is the user 20 who will send, post, place, contribute,publish, author, create, direct, respond, or otherwise cause informationto be distributed to, or made viewable by, one or more other users ofthe information exchange. FIG. 1 is not intended to show every detail ofthe information flow.

The information consumer 28 is the user who will receive informationitems originating from the producers. The consumer 28 may or may notconsume the information items made available to them.

Note that the labels producer and consumer are relative to informationproduction and information consumption and in no way imply a commercialrelationship.

The information item 24 can be a message, email, notice, response, videoclip, audio clip, news, article, story, solicitation, offer,advertisement, URL, or any other form of communication that can be sentor made available by a producer to a consumer.

An information stream is a collection or set of information itemsdelivered sequentially or together to a consumer 28, either directly orembedded, via a medium including, but not limited to, print, email, webfeeds, mobile messaging, video, audio, broadcast, or via any other meansof delivering information.

In FIG. 3 shows an example of an information exchange 29 where user 20may enter user profile data 64 into a system interface for inputting theuser profile 61. The system interface for inputting the user profile 61stores the user profile 60 in a user profile storage 62. The userprofile storage may be an internal part of the information exchange,external to the information exchange, or a combination of internal andexternal. A set of system derived user profile data 63 can also bestored in the user profile storage 62, and in some system, the user maynot input any user profile data.

A user profile 60 includes available information, not limited to form,about the user. This includes but is not limited to behavior,biographic, demographic, historical, ratings, feedback, tracking, orother general or specific information from sources internal and externalto the information exchange 29. The form for the user profile storageincludes relational database, name value pair, no-sql, hierarchicaldata, objects, nested objects, nested hierarchical data, or combinationof databases in a single source or in multiple sources. If accessiblevia an API the user profile 60 may be represented by XML, JSON, CVS, orany other data representation.

The consumer 28 in FIG. 4 may enter a selection criteria data 66 into asystem interface for inputting the selection criteria 68, and aselection criteria 65 is stored in a selection criteria storage 67. Theselection criteria 65 can indicate the type or set of information itemsthat the consumer is potentially interested in or not interested inreceiving. The system interface for inputting the selection criteria 68stores the selection criteria in a selection criteria storage 67. Theselection criteria storage 67 can be internal to the informationexchange 29, external to the information exchange 29, or a combinationof internal and external. Selection criteria can also include a systemderived selection criteria 69 that can also be stored in the selectioncriteria storage 67. In one embodiment, the selection criteria may bestored with the user profile data and the user profile storage andselection criteria storage may be the same.

In one embodiment, the selection criteria storage and user profilestorage may be stored together on contiguous storage for fast access andprocessing.

In FIG. 2, audience targets 50 define a set of consumers or audiencesthat a producer 22 would like to reach or not reach. A system interfacefor inputting the audience targets 44 interacts with a producer limitscontrol loop 46 and an audience target request control loop 48. Theproducer limits control loop 46 and the audience target request controlloop 48 regulate the audience targets 50 included with an informationitem 24 to be processed by a distribution sub-system 52.

A system interface for inputting the information item 40 receives aninformation item 24 from the producer 22. A meta data request controlloop 42 interacts with the system interface for inputting theinformation item 40 and regulates the amount of additional descriptivedata that is collected when an information item 24 is entered. Thedistribution sub-system 52 processes the information item 24, audiencetargets 50, a set of metrics 54, user profiles from the user profilestorage 62, and selections criteria from the selection criteria storageto determine what consumers should get, receive, or view the informationitem as described below. The metrics 54 may be measures, statistics, andparameters obtained, in direct or computed form, from one or moresources internal or external to the information exchange.

In one embodiment, the distribution sub-system 52 and the distributor 26can be the same. In another embodiment, they may be separate.

Operational Description

In one embodiment, the system described here is the information exchangeor an integral part of the information exchange. In another embodiment,the system will exist separately from the information exchange as asub-system interacting with the information exchange as detailed below.

In one embodiment, the system is computer coded software operating on acomputer system. The computer system can be any combination of one ormore physical computer hardware systems, physical servers, devices,mobile devices, CPUs, auxiliary CPUs, embedded processors, workstations,desktop computers, virtual devices, virtual servers, virtual machines,or similarly related hardware with an applicable operating systemappropriate for the specific hardware and, in the case of more than one,interconnected via a private or public network.

In one embodiment, the system may operate as a self regulated automaticcontrol system.

The Producer

In one embodiment, the producer may enter the information item 24 into asystem interface for inputting the information item 40. The informationitem consists of contents and a meta description. The contents caninclude summary, title, full story, image, video, audio, rich media, orother primary information delivery object. The meta description caninclude abstract, source, keywords, authors, bylines, related links,topics, subjects, types, restrictions, pricing or any other fields orobjects or hierarchical data used to classify, categorize, track,identify or otherwise describe the contents and the information item. Inone embodiment, the meta data description and the information item maybe the same.

In one embodiment, the producer may enter the audience target into thesystem interface for inputting the audience targets 44. The audiencetarget describes the consumers that the producer would like to reach ornot reach. The specification of an audience target can reference anyaspect of the user profile to specify the potential consumer. Theaudience target will have an action to specify if the user matching theaudience target should receive the information or not. In oneembodiment, the system interface for inputting the information item andthe system interface for inputting the audience targets may be the same.

In one embodiment, the producer may specify one or more additionalaudience targets that they want. The first audience target is theprimary set of users the consumer wants to include or exclude. Eachadditional audience can have less priority than prior audiencesselected.

In one embodiment, the producer 22 may construct an audience target andpriority by selecting one or more parameters from available data in theuser profile of the consumer and assign a priority to values for eachdiscrete parameters and range of values for continuous parameters. Themax and min values for all combination of field values may be used todetermine a normalized priority scale.

In one embodiment, the producer may first target the largest audiencethey want to reach. The system may set a limit that is less than thesize of the audience specified by the first audience target. In oneembodiment, the limit may be determined by the context of the message,the past history of interactions with the prior messages from theproducer, and current system-wide metrics. In another embodiment, thesystem may adjust the limit in exchange for payment or some otherconcession from the producer. In one embodiment, the producer mayspecify an additional audience target to reach an audience closer insize to the limit. If the size of the audience is less than the limit,the audience target may be used as entered and assigned a priority. Inone embodiment, if the size of the audience is greater than limit thesystem will refine the target to make the audience target meet the limitor adjust the priority of the audience target. In another embodiment,the system may adjust the priority for the audience target if the sizeexceeds the limit. In another embodiment, the producer may scale thepriority based on one or more discrete or continuous parameters used inthe profile of the consumers.

In one embodiment, the producer may have an archive of predefinedaudience targets that can be selected instead of entering and creatingnew audience targets.

In one embodiment, the information items and audience targets may besent to a distribution sub-system. In one embodiment, the distributionsub-system may be integral with the information exchange distributor. Inanother embodiment, the distribution sub-system can be external to theinformation exchange distributor.

In one embodiment, the producer's audience targets may be required. Inanother embodiment, the producer's audience targets may be optional.

In one embodiment, producers may use visual input sliders to indicateaudience targets and priorities for specific profile attributes. Forexample, an audience target with higher priority targets based by yearsof experience of the consumer. In another embodiment, producers may usedrag and drop visuals to rank audience targets and set audience targetpriorities.

In one embodiment, the producer's entered audience target may be appliedto single information item, multiple information items, or allinformation items from that producer.

In one embodiment, the producer may be an autonomous agent.

The User

In one embodiment, the user, producer and consumer, may enter data intothe user profile 60. In another embodiment, the user profile 60 may alsoinclude system data and information about the user including, but notlimited to, performance, behavioral, history, tracking, or any otherinformation that the system can record or compute for a user. In anotherembodiment, the user profile may also include external informationobtained from external systems including, but not limited to,performance, behavioral, history, tracking, records, or any otherinformation that can be obtained or computed from external systems orcombined with internal profile data. In another embodiment, the userprofile may have data from all data sources.

The information exchange user 20 may enter user profile data 64 into asystem interface for inputting the user profile 61. The system interfacefor inputting the user profile 61 stores the user profile data 64 in auser profile storage 62. In one embodiment, the user profile storage maybe part of the information exchange 29. In another embodiment, the userprofile storage 62 may be external to the information exchange 29. Inanother embodiment, the user profile storage 62 may be distributedbetween the information exchange 29 and external to it. In oneembodiment, external and system derived user profile data 63 may bestored in the user profile storage 62.

The Consumer

In one embodiment, the consumer may enter the selection criteria thatdefines the type of information item and may also define a type ofproducer. In another embodiment, the selection criteria may only specifya type of information item or type of producer. In one embodiment, theconsumer may enter an action for the selection criteria to specify ifthe information items matching the criteria are items they would want toreceive or not receive. In another embodiment, the action assigned tothe selection criteria may be assigned by the system from behavioractions of the consumer. For example, by the consumer expressinginterest in an a related item or meta data topic.

The consumer can enter more than one selection criteria. In oneembodiment, if more than one selection criteria is specified theconsumer may specify a priority to define how important the criteria is.Priorities can be expressed by ordering the criteria or by selecting apriority preference input. In another embodiment, the priority of theselection criteria may be assigned by the system from the context of theinputted or derived selection criteria or the behavior, history, oractions leading to the creation of the selection criteria.

The selection criteria may also overlap and conflict. For example, aconflict between criteria may arise if two selection criteria match andone criterion says to include a specific item and another criterion saysto exclude it. In one embodiment, the conflict may be resolved bypreference to the highest priority selection criteria. In oneembodiment, the priorities may be combined in a mathematical function todetermine the priority with the exclude priority optionally multipliedby −1. The function may consider higher weighting for higher prioritiesor may simply average the priorities. If both priorities are the same ina conflict, it may be treated as unresolved or open. In anotherembodiment the conflict may be resolved by the system depending onoptimization criteria discussed below.

In one embodiment, selection criteria and priority for the selectioncriteria may be determined from performance, historical, behavioral, ortracking data of the consumer. In another embodiment, selection criteriaand priority may be determined from predictive statistical methods. Inanother embodiment, selection criteria entered by the consumer may becombined with selection criteria determined from all other means.

In one embodiment, priorities may be set by the system for eachselection criteria. In another embodiment, the system sets a defaultpriority for the selection criteria that can be changed by the consumer.

In one embodiment, the processing of the consumers selection criteriamay be integral with the information exchange distributor. In anotherembodiment, the processing may be external to the default distributor.

In one embodiment, the consumer's selection criteria may be entered by ahuman. In another embodiment, the selection criteria may be entered byan autonomous agent.

The consumer may enter the selection criteria into a system interfacefor inputting the selection criteria. The system interface for inputtingthe selection criteria 68 stores the selection criteria in a selectioncriteria storage 67. In one embodiment, the selection criteria storage67 may part of the information exchange. In another embodiment, theselection criteria storage 67 may external to the information exchange.In another embodiment, the selection criteria storage 67 may distributedbetween the information exchange and external to it. In one embodiment,system derived selection criteria 69 may stored in the selectioncriteria storage 67.

In one embodiment, consumers use drag and drop visuals to rank selectioncriteria and set selection criteria priorities.

In one embodiment, the consumer may be an autonomous agent.

Decision Matrix

In one embodiment, a decision matrix 70 may be used to determine if theinformation item 24 should be included in the information stream of theconsumer 28.

FIG. 5 shows the decision matrix 70 a for a basic case with no audiencetarget priorities or selection criteria priorities.

In FIG. 5, the producer's 22 actions for the two audience targets 50 areshown along the horizontal. The two audience targets 50 are for a sendand a do-not-send action. The letter ‘S’ indicates the send action andthe letters ‘DS’ indicate the do-not-send action. The letter ‘O’ foropen indicates the case where no audience target applies to theinformation item.

In FIG. 5, the consumer actions for two selection criteria 65 are shownalong the vertical. The two audience targets 50 are for a want and ado-not-want action. The letter ‘W’ indicates the want action and theletters ‘DW’ indicate the do-not-want action. The letter ‘O’ for openindicates the case where no selection criteria 65 applies to theinformation item 24.

In FIG. 5, the decision matrix 70 a used to indicate when theinformation item 24 should be included in the information stream of theconsumer 28 or excluded from the information stream. In the decisionmatrix 70 a a letter ‘I’ indicates inclusion of the information item 24into the information stream, and the letter ‘E’ indicates exclusion ofthe information item 24 from the information stream. The symbol ‘?’indicates that the system may decide if the information item 24 isincluded or not.

In FIG. 6, the table extends the table shown in FIG. 5 to showpriorities for the audience targets 50 and selection criteria 65. Theproducer's audience targets 50 are again along the vertical and theconsumers selection criteria 65 are along the horizontal. The producer'saudience targets 50 are shown with combined action and preferencepriority. The letter ‘H’ indicates a high priority. The letter ‘M’indicates medium priority. The letter ‘L’ indicates low priority. InFIG. 6 six audience target action and priority combinations are shownfor the producer 22. For the consumer six selection criteria action andpriority combinations are shown. As in FIG. 5, the case where no actiontargets 50 applies to the information item 24 and the case where noselection criteria 65 applies to the information item are also shown inthe table.

The decision matrix 70 b has the same meaning as in FIG. 5 but has addedcombined symbols to show cases where the system might override thedefault. The symbols ‘I?’ indicate a case where In one embodiment, thedefault would be to include the item in the stream but the system maydecide to switch the decision. The symbols ‘E?’ indicate a case where Inone embodiment, the default would be to exclude the item from thestream, but the system may decide to switch the decision. The othersymbols shown in FIG. 6 have the same meaning as in FIG. 5.

There is no limit on the number of discrete priority levels that can beassigned to audience targets 50 or selection criteria 65. Fewer prioritylevels may also be allowed so that a combination between the tables inFIG. 5 and FIG. 6 is possible. For the decision matrix 70 with discretepriority level the system can choose what cells to override.

In one embodiment, the priorities may be determined from a continuousfunction of variables from the producer's user profile 60, theconsumer's user profile 60, the meta tagging for the information item24, external factors, or any other data available to the system. Thepriority from the continuous function can be of any scale, and the scalecan be infinite or fixed or normalized, for example normalized to thezero to one interval.

For the case of continuous priorities the decision matrix 70 c maycontain a logical function for each combination of actions for theproducer 22 and the consumer 28 in the decision matrix 70 c as shown inFIG. 7. The logical function can evaluate the priorities for theproducer and consumer actions along with other factors discussed belowto determine if the information item 24 is included in or excluded fromthe stream.

Any combination of the decision matrix 70 shown in FIG. 5-7 may bepossible. For example, the consumer might have several priority levelsfor a want action one priority for the do-not-want action and theproducer may have continuous priority for the send action and threepriorities for the do-not-send actions.

In one embodiment, the processing of the decision matrix 70 may beintegral with the information exchange 29 default distributor 26. Inanother embodiment, the processing of the decision matrix 70 mayexternal to the default distributor 26. In one embodiment, theprocessing of the decision matrix 70 may distributed between the defaultdistributor and an external process. In another embodiment, the decisionmatrix 70 can be partially evaluated to identify eligible consumers andthe remaining processing of the decision matrix 70 can be completed torefine the consumers that will have the item included in their stream.

In one embodiment, the producer sees the descriptions for the do-notsend priorities similar to “never send”, “prefer not to send”, “ok ifthey get it but dont include in my counts” translating to high, medium,and low priority.

In one embodiment, the action priorities for a consumer want action maybe represented with descriptions similar to “must have”, “ok to have”,“give to me if it might be important” translating to high, medium, andlow priority.

In one embodiment, a decision grid 70 d represents the decision matrix70 for the case of discrete, continuous, or mixed priorities as a twodimensional interval with a range of [1,−1] for each dimension. Theactions for do-not-want and do-not-send multiply their priorities by −1and the open case is represented by 0. The two dimensional interval isequivalent to any non-normalized two dimensional interval. A thresholdline 71 separates the interval from an include region 72 and an excluderegion 73. The threshold line or boundary can be derived from themetrics 54 and can be represented by a threshold function, mapping, orrelation.

In one embodiment the exclude region 73 may be divided into a reachableexclude range and a non-reachable exclude range. The reachable excluderange may be defined as the part of the exclude region below thethreshold line 71. The reachable exclude range may also be defined asthe part of the exclude range that may be reached by the producer, ifthe producer can increase the priority of the audience target matchingthat consumer.

In one embodiment, there may be a priority bounds in the decision gridor decision matrix where the threshold line may not cross.

In the discrete case the threshold is a set of cells that form theboundary of the include region 72 and exclude region 73. For example, inFIG. 6. the threshold set would be the boundary along any row or columnwhere there is a switch from include to exclude. A range or subset ofthe decision matrix 70 is a set of cells or regions in the twodimensional interval.

Use of Metrics

In one embodiment, a consumer participation metric may be used as ameasure of information item consumption or interaction with theinformation item. The consumer participation metric may be obtained orcomputed from views, interactions, clicks, opens or any other applicableindicator of information item consumption by the consumer and useful tothe information exchange. In one embodiment, the participation metricmay be exact. In another embodiment, the participation metric may beestimated. In one embodiment, the participation metric may be the numberof items participated in for a specified period.

In one embodiment, the consumer participation metric for specificperiods useful to the information exchange may be stored in database. Inone embodiment, all historical data used for computing or obtaining theconsumer participation metric may be stored in a database.

In one embodiment, a participation rate for the information consumer 28may be measured as the number of information items participated individed by the number of information items delivered or sent or madeavailable to the consumer over a specified period (for example a day).In one embodiment, the participation rate may be obtained from othersources including surveys, monitoring, or other internal and externalmetrics.

In one embodiment, an historical participation rate may be computed foreach consumer. The historical participation rate can be computed in anynumber of ways from prior participation of the consumer. For exampleusing weighted history, rolling average or other computations. Multiplemeasures of historical participation can be used. In one embodiment, thehistorical participation rate for each consumer may be maintained in adatabase. In one embodiment, all historical data used for computing orobtaining the consumer participation rate metric may be stored in adatabase.

In one embodiment, a consumer item value for the information item may beestimated for the consumer using the priority established from theselection criteria of the consumer. In one embodiment, the priority ofthe information item may be the highest priority of matching selectioncriteria. In another embodiment, the consumer item value may be computedfrom the priority of over lapping selection criteria. In one embodiment,the item value may be computed from the priority and other metrics.

In one embodiment, a mapping of priority to value for the consumer maybe used. In another embodiment, the consumer value and priority may beassumed to be equivalent.

In one embodiment, an average consumer item value may be computed for aperiod of time. The average consumer item value may be computed as thesum of the consumer item value for items participated in for the perioddivided by the number of items participated in for the period. In oneembodiment, a weighted average may be used to compute the averageconsumer item value with weights depending on information item meta dataor other metrics. In one embodiment, a historical time series of averageconsumer item value may be computed. In one embodiment, the historicaltime series of average consumer item value may be maintained in adatabase.

In one embodiment, the historical time series of average consumer itemvalue may be used to estimate a consumer expected item value for aninformation item the consumer has not yet received. Multiple formulaspecific to the information exchange can be used for this estimate. Forexample using weighted history, rolling average or other computations.Multiple measures of the expected item value can be used. In oneembodiment, the expected item value may be computed from the historicalaverage consumer item value and other metrics.

In one embodiment, the expected item value may be computed or obtainedfrom, surveys, sentiment analysis, or other metrics.

In one embodiment, a predictive participation rate may be computed. Inone embodiment, the predictive participation rate may be derived fromstatistical or predictive analytics using the historical participationrate and internal and external metrics and signals. In one embodiment,the predictive participation rate may be the same as the historicalparticipation rate.

In one embodiment, a participation prediction mapping may relate theexpected item value to a predicted participation level. The predictedparticipation level represents a number of information items perspecified period. The participation prediction mapping may be adiscrete, continuous, or mixed logical function or mapping. In oneembodiment, statistical methods appropriate to the information exchangemay be used to compute and derive a predictive participation formula ormapping using the consumer expected item value and additional internaland external metrics and signals. In one embodiment, the participationprediction mapping may be determined using metrics from other consumers.

In one embodiment, a inverse participation prediction mapping may beused to relate the participation level to an expected item value.

In one embodiment, a producer item value per consumer may be the valueto the producer for the consumer to receive and consume an informationitem. The producer item value may be computed using the priorityestablished from the audience targets for that information item. In oneembodiment, the producer item value for a consumer may be computed fromthe priority and other metrics.

In one embodiment, a mapping of priority to the producer item value perconsumer may be used. In another embodiment, the producer value andpriority may be assumed equivalent.

In one embodiment, a distribution of information items on the twodimensional decision matrix 70 or decision grid may be computed for eachconsumer. The distribution records the number of information items for atime period for each point in the decision matrix 70 or decision grid 70d. Any number of techniques specific to the information exchange can beused for recoding the distribution based on historical data. For exampleusing weighted history, rolling average or other computations. Multipledistributions are possible and can be used for different purposes incomputing other metrics. In one embodiment, aggregations ofdistributions across consumer may be used.

In one embodiment, the historical distribution of information items andoptional additional metrics may be used to compute a predicteddistribution of information items for a consumer in a current period ora future period. In one embodiment, the distribution of informationitems for specified future period may be predetermined.

In one embodiment, a targeted consumer expected item value may becomputed from the metrics to determine the information exchange desiredexpected item value for each consumer.

In one embodiment, the threshold line 71 of the decision matrix 70 ordecision grid 70 may be computed using the predicted distribution ofinformation items for a consumer, the mapping of priority to value forthe consumer, the mapping of priority to value for the producer, theparticipation prediction mapping, the consumer expected item value, thetargeted expected item value, or other metrics.

In one embodiment, an exchange value function may be specified toindicate a combined value to the exchange for each point on the grid.For example, the exchange value function might be T(p,c)=ap+bc wherep=producer value, c=consumer value, a=1 if p>0 and 2 if p<0, b=1 if c>0and b=2 if c<0. Functions of this type encapsulate the trade off whenconsumer item value or producer item value is negative. Other functionscould be used depending on the goals of the information exchange and thefunction could vary by consumer, temporal parameters, or other internalor external parameters particular to the exchange. In one embodiment,the exchange value function may define the priority bounds.

In one embodiment, a number of information items over a region of thedistribution of information items may be computed as the sum over everypoint in the region. For example the distribution might indicate thatthe number of items is 5, 4, 7, 3, 11 for 5 points defining theparticular region. The sum of information items over this region is 30.

In one embodiment, an average consumer value over a region of thedistribution of information items may be computed as the sum of theconsumer value times distribution value over every point in the regiondivided by number of information items in the region.

In one embodiment, the threshold line 71 may be computed for thedistribution of information items as the region inside the decision grid70 d where the predictive participation level from the predictedparticipation mapping for a specified expected item value isapproximately equal to the number of information items in the region. Inone embodiment, the specified expected item value may be the averageconsumer value over the region of the distribution. In anotherembodiment, the specified expected item value may be determined frominternal and external metrics.

In one embodiment, the include region 72 may be chosen for a specifieddistribution of information items by first dividing the decision grid 70d into discrete points. For example to divide the decision grid consumerand producer priority axis into 10ths would yield 20×20 or 400 discretepoints. For the decision matrix 70 use the cells as the discrete points.Second, evaluate the exchange value function at each discrete point onthe decision grid to determine the points to include first in theregion. Third, sequence through the points in descending preferenceorder and compute the number of information items over the regionincrementally at each point, and also compute the expected item valueusing the average consumer value over a region or other metrics to getthe expected item value. Fourth, evaluate the predictive participationlevel from the predicted participation mapping for the specifiedexpected item value, and when predictive participation level is lessthan the number of items stop. Fifth, use the processed points to definethe include region 72 and the threshold line 71.

In one embodiment, the distributions of information items that may berelevant for the consumer may be stored in a database. The distributionsmay be updated in real-time. The threshold line 71 may be updated inreal time as the distributions or other metrics change.

In one embodiment, a consumer data collection may include the selectioncriteria, distributions, decision matrix, threshold line, and otherconsumer metrics. In one embodiment, the consumer data collection may bestored on contiguous storage for fast access and processing.

In one embodiment, a consumer audience details query method can be usedto evaluate the information item, the audience targets, the consumerdata collection, or other internal metrics to determine a set ofconsumer audience details that may include but not limited to theconsumer priority, the producer priority for that consumer, the producerpriority on the threshold line (if applicable), and an indicator of therange (exclude, include, or reachable exclude).

In one embodiment, the consumer audience details query may use a list ofmeta tagging groups, fields, and values to evaluate a consumer priorityresponsiveness to meta tagging. The consumer audience details querymethod may logically evaluate the list of meta tagging groups, fields,and values against the selection criteria to determine the consumerpriority that would be assigned to each meta tag option in the list andmay also include relevant combinations. The consumer priorityresponsiveness to meta tagging may include priority levels the items inthe list and may also include summaries by field, group, and selectioncombinations.

In one embodiment, an audience details query method can evaluate theconsumer audience details method for each consumer to compute andaggregate a set of audience details. The audience details can bepresented to the meta data control loop, the audience limits controlloop, the audience target control loop, the system interface forinputting audience targets, or the system interface for inputting theinformation item.

In one embodiment, a sufficiently sized statistical sample of theconsumer's selection criteria and consumer data collections may be usedinstead of actual consumer data to provide an estimate of the audiencedetails.

In one embodiment, the audience details for individual audience targetsmay include, but not limited to, raw audience size, incremental audiencesize, accumulated audience size, audience size limits imposed priority,audience size in the include range, audience size in the reachableexclude range, or average producer priority change needed to move fromreachable exclude region. The audience details for all audience targetsspecified may include, but not limited to, max audience size for alltargets, or accumulated size for all targets. The audience details foran information item may include, but not limited to, distribution ofconsumer priority for the information item, user profile summarystatistics for specific priority ranges, or priority responsiveness tometa tagging.

In one embodiment, the consumer priority responsiveness to meta taggingcan be aggregated and summarized across all consumers to get theresponsiveness to meta tagging values.

Control Loops

A set of control loops use metrics 54 to control the flow of informationitems in the information exchange. The metrics are measures andparameters that may be internal to the information exchange or externalto it. Sample internal metrics include, but are not limited to, metricsrelated to producer, consumer, system information flow, or theinformation exchange in general. Sample external metrics include, butare not limited to, indications of important sporting events occurringthat day, severe weather, day of week, political or business eventsoccurring, measures of news and information flow or activity external tothe information exchange, flow activity on external informationexchanges, historical projections, statistics, or any other relevantdata.

One embodiment may have multiple control loops. Another embodiment mayhave a single control loop. Another embodiment may have no controlloops.

Decision Matrix Control Loop

In one embodiment, a decision matrix control loop adjusts the thresholdline 71 or the boundary of the include region 72 and exclude region 73in the decision matrix 70 or decision grid 70 d to improve or maintain aset of success metrics.

In one embodiment, if the point on the decision matrix 70 or decisiongrid 70 d represented by the selection criteria priority and audiencetarget priority is within the include region 72 defined by the thresholdline 71 for the consumer, the information item is included in theconsumer's information stream.

In one embodiment, the decision matrix control loop may use the set ofsuccess metrics derived from the consumer, the producer, the informationitem, audience targets, external sources, or from the system in general.The metrics related to the consumer include, but are not limited to, thetime processing the information stream, estimate of missed informationitems, participation metrics, participation rate, average selectioncriteria priority for the information stream over recent and historicalperiods, the consumer expected item value, predicted participation rate,predicted participation level, or other consumer metrics. The metricsrelated to the producer include, but are not limited to, user profiledata including producer historical, performance, or behavioral data. Themetrics from external sources could include, but are not limited to,indications of important sporting events occurring that day, severeweather, day of week, political or business events occurring, measuresof news and information external to the information exchange, or anyfactor deemed relevant to the prediction of the attention and focus ofthe consumer. In another embodiment, only some of the metrics may beused or only one metric may be used.

In one embodiment, the decision matrix control loop may be part of thedistribution sub-system 52.

In one embodiment, for each information item 24 processed by thedecision control loop a consumer priority may be obtained from theconsumer's selection criteria 65 and a producer priority may be obtainedfrom the audience targets 50 for that information item.

In one embodiment, multiple information items may be processed at onetime as a distribution of information items on the decision grid 70 d ordecision matrix 70 and the include region 72 can be computed todetermine which information items may be included in the consumer'sinformation stream. In one embodiment, information items may be delayedor queued to be evaluated together as a distribution of informationitems.

In one embodiment, an estimated value of the probability the informationitem will be missed, meaning it will be received but is not be processedby the consumer, may be derived from the metrics. A system limit for theprobability the information item will be missed may be derived from themetrics. Within the specified range or subset of the decision matrix 70,if the estimated value of the probability the information item will bemissed is greater than the system limit for this estimate theinformation item is excluded. In one embodiment, within the specifiedrange or subset of the decision matrix 70, if the consumer item value isgreater than the consumer expected item value, the information item isincluded.

Producer Limits Control Loop

The producer limits control loop 46 determines an audience size limit tobe placed on the producer's audience targets 50 at a particular prioritylevel.

FIG. 9 shows an embodiment where the audience size limit may berepresented as an audience size limits mapping 75. The audience sizelimits mapping 75 can be used to get a priority for a given audiencesize or to get an audience size for a given priority. The audience sizelimits mapping 75 can be a function or relation between priority and theaudience size limit. The mapping can be continuous, discrete, or mixed.FIG. 9 show the audience size limits mapping 75 as a continuous mapping.

In one embodiment, the audience size limits mapping 75 may be firstdetermined by the metrics 54. The metrics 54 can include, but is notlimited to, the current number of information items flowing through thesystem, the relative number of consumers receiving too few or too manyitems, or the predicted number of information items flowing through thesystem in the future. The audience size limits mapping 75 may beadjusted using meta data and contents of the information item 24, andmay be further adjusted using metrics from the producer's user profilethat include, but are not limited to, expertise, background, reputation,number of prior sends by the producer, interaction rates or performanceof past information items sent by the producer.

In one embodiment, the audience size limits mapping 75 may bedynamically determined in real time. In one embodiment, a base audiencesize limits mapping may be set by an administer and the base set oflevels may be adjusted or not adjusted in the producer limits controlloop 46.

In one embodiment, max audience size 76 may be determined from thelargest limit in the audience size limits mapping 75 with positivepriority. In one embodiment, the total audience the producer could reachmay not exceed the max audience size 76.

FIG. 10 shows one embodiment of the system interface for inputting theaudience targets 44. Options 90 allow the producer 22 to create audiencetarget 91, edit audience target 92, reorder audience targets 93, manageaudience target archive 94, view audience target details 95, viewaudience size limits 96, or complete the process when done 97.

In one embodiment, the producer 22 may interact with the producer limitscontrol loop 46 to first create an audience target or retrieve anaudience target from the archive. After the first audience target isentered, the audience target can be evaluated and the audience targetdetails 95 can be viewed and processed by the producer 22. The producer22 can accept the priority level, reorder the audience targets 93, oredit the audience target 92. If the priority level for the audiencetarget is accepted, the producer has the option to enter an additionalaudience target. If the producer enters the additional audience target,the process used for the first audience target can be repeated. If thecombined audience from all audience targets exceeds the max audiencesize 76, the producer can reorder the audience targets 93 or edit theaudience targets 92. In one embodiment, each additional audience targetmay have lower or higher priority than the first audience target entereddepending on the order and audience target action.

In one embodiment, the audience targets may be evaluated by the orderspecified by the producer to determine an incremental size for thataudience target. The incremental audience size can be the size of theadditional audience that can be reached by each subsequent lower orderedaudience target. FIG. 9 shows the ordered audience targets as A1-A5. Asan example consider the audiences for audience targets A1, A3, and A4 asaudience targets where the producer wants to include or send theinformation item and A2 and A5 as audience targets where the producerwants to exclude or not send. The audiences for targets A1-A5 areevaluated to obtain the incremental size. The incremental size for theaudience targets with an include action (A1, A3, and A4 in this example)may be accumulated down the order to get an accumulated size for theaudience target. The incremental audience excludes any consumer thatwould match one of the audience targets of a higher order. In oneembodiment, the accumulated size for the audience targets with theinclude action adds the incremental size for that audience target aswell. The accumulated size can then be evaluated from the audience sizelimits map 75 to determine the priority for that audience target. InFIG. 9, the audience targets A1, A3, and A4 have priorities P1, P3, andP4 assigned respectively. In one embodiment, the accumulated size may beused as a lookup size to get the priority from the audience size limitsmap 75. In one embodiment, accumulated size may be adjusted by afraction of, or all of, the incremental size.

In one embodiment, the incremental size for the audience targets with anexclude action (A2 and A5 in this example) may be accumulated down theorder to get an accumulated size for each audience target with theexclude action. In one embodiment, the accumulated size for the audiencetargets with the exclude action does not add the incremental size forthat audience target. The accumulated size can then be evaluated fromthe audience size limits map 75 to determine the priority for thataudience target. In FIG. 9 the audience targets A2 and A5 havepriorities P2 and P5 assigned respectively.

In one embodiment, the producer may assign any priority to audiencetargets with an exclude action.

In one embodiment, the audience targets may be automatically adjusted toimprove the audience size while using the audience size limits mapping,by first identifying an audience that would be included at a lowpriority; second, generating the audience target for the identifiedaudience; third, assigning that audience target a priority directly andplacing it in the appropriate audience target order; and fourth, excludethe incremental audience from the accumulated size, but exclude it fromthe incremental counts of the subsequent targets.

In one embodiment, audience targets with an include action may beprocessed provided the priority is >0. In one embodiment, the audiencetarget that may have accumulated sizes that exceed the max audience size76 by less than the incremental size for the audience target may belimited by the system to a size that will not exceed the max audiencesize 76. In another embodiment, the producer may have the option torefine the audience target that crossed the limit.

The audience size for an audience target could be an estimate in oneembodiment, or an exact number in another embodiment.

In one embodiment, the number of audience targets may be limited.

In one embodiment, the producer enters one or more audience targets. Thesystem then automatically ranks the audience targets by audience sizeand assigns priorities using the methods above.

In one embodiment, the producer may specify a priority preference over arange of user profiles as F(X) where X is user profile from a span ofuser profile characteristics. For example specifying age between 30 and40 with 30 being the most preferred. Using F(X) a rank of everypotentially targeted user profile is obtained. The mapping betweenpriority in the [0,1] interval and audience size limits mapping 75 isused to assign a priority to each incremental target from highest rankto lowest and stopping when the max audience size 76 or the span of X isreached.

Methods for Determining the Audience Size Limit Mappings

In one embodiment, the audience size limit mapping 75 may be determinedusing the reverse cumulative distribution of the consumer populationdensity over the priority on a normalized interval [−1,1] for theinformation item. The reverse cumulative distribution is the number ofconsumers whose selection criteria would register a given priority orhigher for the information item. The consumer population density for aninformation item is the number consumers at each consumer prioritylevel, and this can be obtained by accumulating the counts of consumersat each priority level. The reverse distribution is then obtained fromcumulations of the population density starting at the top of theinterval [−1,1]. In one embodiment, only the population density andreverse cumulative distribution on the interval [0.1] may be needed. Inone embodiment, consumers with similar selection criteria may beaggregated in a map reduced representation with the representativeselection criteria and the number of consumers. This is so that only onerepresentative consumer needs to be evaluated for the set of similarconsumers. In one embodiment, the reverse cumulative distribution may beused directly as the audience limit mapping. In another embodiment, thereverse cumulative distribution may be scaled or adjusted before beingused as the audience limit mapping. The advantage of using reversecumulative distribution is that it provides higher limits to theproducer 22 that would have a natural consumer priority for theinformation item 24. For example, a popular merchant providing aninformation item about a free give away or a popular news organizationwith exclusive breaking news might have a large number of informationconsumers 28 who place a high priority on receiving such informationitems. In such a case the producer may not be required to enter anyaudience target at all because the default priority level is alreadysufficiently high. On the other hand, a product vendor with aninformation item 24 that is a marketing message of value to only a smallgroup of information consumers may have a very restrictive audiencelimit mapping and may need to input very specific audience targets.

In one embodiment, the audience limit mappings may be determined fromthe producer's send history, past activity and keyword or othermechanical analysis of the information item.

In one embodiment, with discrete or continuous audience size limits theparameters of the audience size limits mapping may be determined fromthe metrics. For example in the continuous case a linear relationshipbetween size and priority could be used and the metrics would determinethe slope and intercept of the line. More specifically, in this linearexample, with the priority on a [0.1] interval the parameters would bethe priority level for the max audience size 76 and at audience sizezero. Other mathematical functions, mappings and parameterizedrelationships can be used also, with the parameters of such functions,mappings, and relationships determined by metrics in a similar manner.

Producer Request Control Loops

The producer request control loops may consider the impact thatrequesting the producer to provide additional meta data and audiencetargets has on the number of information items the producer may send orcontribute over time as well as the perceived impact of the additions tothe information item with the consumer. The producer request controlloop may use metrics that include, but are not limited to, theopportunity cost of time for the producer, the time the producer istaking to complete a send, the time to get additional requested data,the availability of additional data, the value of additional metatagging or refinements, time the producer takes to enter a new audiencetarget, or the time taken to change, rank or prioritize audiencetargets. Consumer perception, participation, or response metrics mayalso be used.

In one embodiment, the producer request control loop may be used forcontrolling the input of the information item meta data via the metadata request control loop 42. In one embodiment, the producer requestcontrol loop may be used for controlling the input of the audiencetargets 50 through the audience target request control loop 48. In oneembodiment, the producer request control loops may supplement theproducer limits control loop 46. In another embodiment, the producerrequest control loops may be an alternative to the producer limitscontrol loop 46.

In one embodiment, a subject domain for the information item may bedetermined by automated analysis of the information item as entered bythe producer. The subject domains may be used to get a list of metatagging schema and usage data for the subject domain that wasdetermined. The list of meta tagging schema and usage data may be usedto generate the list of meta tagging groups, fields, and values thatcould be provided to the audience details query method. Theresponsiveness to meta tagging with audience size increments for thespecified meta tagging that may be provided by the audience detailsquery method can be used to get the audience size increments that can beused to select the order that the meta tagging questions are requested.

In one embodiment, the list of meta tagging schema may be general. Inanother embodiment, the meta tagging schema may be subject domainspecific.

In one embodiment, the list of meta tagging schema may be selected tocomplete meta data that could not reliably be provided by the automatedanalysis, or to confirm known fields, or to confirm the expertise of theproducer. This can also be used to limit autonomous and non-autonomousproducers.

In one embodiment, the producer may not see the audience sizesassociated with the specified meta tagging.

In one embodiment, a desired audience distribution may be used toregulate the producer request control loops. The audience distributioncan be parameterized by statistical measures or metrics to quantifydesirability. For example, to have a distribution over consumerpriorities interval that reduces the density at or near zero.

In one embodiment, the meta data request control loop may use theaudience distribution over the consumer priority interval. Using theaudience distribution over the consumer priority interval has advantagesin that the well know producers with naturally high audiences andreceptiveness can avoid extra requirements for meta tagging or audiencetargets and the associated time burden.

In another embodiment, either the meta data request control loop or theaudience request control loop may use the audience distribution over thetwo dimensional decision grid or decision matrix, and the desiredaudience distribution can be over both consumer and producer priorities.The information producer can use either the meta data request controlloop or the audience request control loop to reach a the desiredaudience distribution.

In one embodiment, any required meta data requests or audience targetrequests may stop when either the desired audience distribution has beenreached or a maximum number of requests to the producer has been made.

The subject domain may be compared to the subject domains that can beinferred from the user profile of the producer and the prior informationitems produced by the producer. If the implied subject domains of theinformation item do not align with the subject domains of the producerand the history of the producer the producer's history the meta dataquestions may be asked to confirm the validity of the post and thesender.

Control Loop Admin

An administrator of the information exchange can select the controlloops to use and set the configurations for the control loops. Thesuccess metrics can be targeted to balance the value to the differentusers of the exchange with the goals of the information exchangestakeholders.

CONCLUSION

The computer system described here is broadly applicable to existinginformation exchanges or as basis for new information exchanges tooptimize and better engage participants.

Examples and variations given in this specification are not limiting andother examples and variations will be apparent to those skilled in theart.

1-19. (canceled)
 20. A method for using a computer system fordetermining inclusion of an information item to an information stream ofan information consumer comprising: determining for the information itema producer priority relating to the information consumer; determiningfor the information consumer a consumer priority relating to theinformation item; determining whether to include an information iteminto the information stream of the information consumer for the producerpriority and the consumer priority according to a decision matrix;obtaining a participation prediction map relating expected item valuefor the information consumer and predicted participation; obtaining adistribution of information items over a range of consumer priority andproducer priority; adjusting the decision matrix, wherein an includeregion for the decision matrix is determined, such that (a) any itemfrom the distribution of information items in the region is morepreferred to an item not in the region according to an exchange valuefunction (b) a participation metric related to the number of items fromthe distribution of information items in the region is approximatelyequal to a predicted participation from the participation prediction mapfor a derived expected item value.
 21. The method of claim 20 wherein atleast one audience target indicating a producer priority is obtained forthe information item.
 22. The method of claim 20 wherein the producerpriority with numerical value: (a) less than 0 indicates an excludeaction, (b) greater than 0 indicates an include action, (c) equal to 0indicates an indifference to an action.
 23. The method of claim 20wherein at least one selection criteria indicating a consumer priorityis obtained for the information consumer.
 24. The method of claim 20wherein the consumer priority with numerical value: (a) less than 0indicates an exclude action, (b) greater than 0 indicates an includeaction, (c) equal to 0 indicates an indifference to an action.
 25. Themethod of claim 21 wherein the audience target represents a mappingbetween attributes in the information consumer user profile and a rangeof priority values.
 26. The method of claim 23 wherein the selectioncriteria represents a mapping between attributes in a user profile of aninformation producer and a range of priority values.
 27. The method ofclaim 23 wherein the selection criteria represents a mapping betweenattributes in the information item and a range of priority values. 28.The method of claim 20 wherein the decision matrix resolves conflictsbetween an audience target and a selection criteria.
 29. The method ofclaim 20 wherein participation corresponds with a rate of informationitems processed by the information consumer.
 30. The method of claim 20wherein the decision matrix is a continuous two dimensional regionrepresenting producer priority on one dimension and representingconsumer priority on the other dimension.
 31. The method of claim 30wherein the dimensions are between −1 and
 1. 32. The method of claim 20wherein a threshold boundary divides the region into an include regionand an exclude region.
 33. The method of claim 32 wherein the thresholdboundary is dynamically adjusted in real-time.
 34. A method in acomputer system for assigning priority to an audience target for usewith at least one information item comprising: obtaining multipleaudience targets; obtaining an order for the audience targets whereinthe audience targets are ranked; obtaining a derived audience sizerelating to each audience target; determining the priority for eachaudience target using the derived audience size for said audience targetand an audience size limits map, wherein the audience size limits maprelates audience size and priority; whereby the multiple audiencetargets are assigned a priority.
 35. The method of claim 34 wherein saidaudience size limits map assigns lower priorities to larger audiencesizes.
 36. The method of claim 34 wherein the order of audience targetsis obtained from an information producer.
 37. The method of claim 34wherein for each audience target with an include action an accumulatedsize is computed as the union of audiences matching higher orderedaudience targets with an include action and said audience target andused as the derived audience size to determine the audience targetpriority from the audience size limits map.
 38. The method of claim 37wherein said accumulated size excludes the intersection of audiencesmatching higher ordered audience targets where the audience targetaction is exclude.
 39. The method of claim 34 wherein the audiencetarget priority is between −1 and
 1. 40. A computer system apparatus forregulating the exchange of at least one information item between aninformation producer and an information consumer comprising: aninformation exchange; an information stream; a decision matrix; adecision matrix control loop; a means for determining inclusion of theinformation item in the information stream of the information consumer,wherein the means for determining inclusion uses the decision matrix; ameans for predicting participation of an information exchange user; ameans for determining a success metric, wherein said success metric isderived from predicted participation; and a means for controlling theinformation stream, wherein the means for controlling uses the decisionmatrix control loop to adjust the decision matrix to improve or maintainthe success metric.
 41. A computer system apparatus for assigning apriority to an audience target comprising: a means for obtaining theaudience target; a means for obtaining a derived size for the audiencetarget; an audience size limits map relating audience size and thepriority; a means for ordering multiple audience targets; a means fordetermining the priority for the audience target using the derived sizeand the audience size limits map.
 42. The apparatus of claim 41 whereinthere is a means for computing the audience size limits map.
 43. Themethod of claim 20 wherein (a) an include action indicates a priority 1,(b) an exclude action indicates a priority of −1, (c) no action orindifference to an action indicates a priority of
 0. 44. The method ofclaim 20 wherein the information stream is further restricted to aspecified number of information items for a period of time.
 45. Themethod of claim 44 wherein the specified number of information items isvariable.
 46. The method of claim 44 wherein the information itemsselected to meet the restriction are those that have the highest valuefor the exchange value function.
 47. The method of claim 20 wherein thederived expected item value is determined from the information items inthe region.